1. Technical Field
This invention relates to image analysis systems, and more particularly to automated optical character recognition systems.
2. Discussion
Over the last few years, applications of machine vision to the areas of automated inspection and manufacturing have been increasing considerably. Among the areas of machine vision most pursued by various industries are: optical character recognition, defect inspection, gauging and, product counting. Of all these applications, optical character recognition ("OCR") has been of great importance to many industries, such as, pharmaceutical and packaging industries for a variety of reasons. Some of these reasons are:
(1) better quality of products, PA1 (2) cost effectiveness of products, PA1 (3) consistency of product quality, PA1 (4) proper documentation of errors and PA1 (5) better productivity. PA1 (1) recognition irrespective of orientation of characters, PA1 (2) recognition irrespective of position of characters, PA1 (3) very high speed recognition of characters, PA1 (4) recognition irrespective of position and orientation of characters, PA1 (5) recognition of degraded or poor quality characters, PA1 (6) accurate recognition, and PA1 (7) recognition of symbols, as well as, characters. PA1 (1) image acquisition with a TV camera, PA1 (2) segmentation of the acquired image to extract the character image, PA1 (3) processing of the character image by vector correlation technique to extract data pertaining to individual characters, PA1 (4) analysis of the vector correlation data to recognize the characters and, PA1 (5) display the recognized character data. PA1 (1) perform image processing pertaining to orientation independence to extract a blob image, PA1 (2) compute the orientation of the character string by computing the orientation of the principal axis of the blob and, PA1 (3) rotate the characters about an axis to orient it horizontally across the image for recognition. PA1 (1) locate a mark or "fiducial" in the entire image, PA1 (2) compute the displacement of this mark from its "nominal" position and, PA1 (3) move the character image by this displacement to place it at its "nominal" position. PA1 (1) perform image processing pertaining to orientation independence to extract a blob image, PA1 (2) compute the orientation of the principal axis of the blob from horizontal. PA1 (3) rotate the character image about an axis to orient it horizontally across the image, PA1 (4) locate a mark or "fiducial" in the entire image, PA1 (5) compute the displacement of the "fiducial" from its "nominal" position, PA1 (6) move the character image by this displacement to place it at its "nominal" position.
In today's high speed manufacturing processes, it is important to maintain the quality of products without affecting the rate of productivity. Everyday stricter conditions are laid down for better product quality, particularly for the pharmaceutical industry. In this manufacturing environment, it is important to have correct labels and numbering on parts manufactured by an industry. Also, it is increasingly difficult for human inspectors to keep up with the speed of the manufacturing processes. With machine vision application of OCR technology, the speed of a manufacturing process can be maintained without relying on human inspectors. This requires less inspectors and a more cost effective manufacturing. Further, a machine vision system generates results in a consistent way and all errors are automatically documented for future processing.
Many different types of OCR equipment have been installed in different types of industrial environments over the past several years. This equipment includes optical sensing devices, such as television (TV) cameras, image processing units, display monitors for training and viewing and, other peripheral devices. The cameras are mounted in a given orientation to view the manufactured parts and then read characters from these parts in a fixed orientation. Also, character recognition on these parts is performed at a fixed location in the manufacturing line. This constrains the manufacturers to place the characters in a given orientation and at a very precise location on the parts.
These two constraints may not be achievable in many manufacturing scenarios. One example of such a scenario is the task of reading characters from bottle caps as these bottles come down a manufacturing line. The bottles rotate continuously as they move down this line. Thus the character set printed on the caps, which is to be recognized by the machine vision unit, also rotates continuously down the line. There is no guarantee that the character set will be presented at a fixed orientation in front of the camera. We can also have many situations where a character set is not printed at the same precise location on a part. An example of this would be characters stamped on a part by a stamping machine. Errors in the motion of the stamping machine or non-uniform movement of the part along the line, may cause the characters to be printed at different positions of the part at different times. Thus if the TV camera points to a fixed position on the part, it may not find the entire set of characters on the part even though all the characters are present on the part.
In order to overcome most (if not all) of the drawbacks of present OCR technology, it would be desirable to have an image analysis system which is capable of orientation and/or position independent character recognition.